Nowadays, enterprises and public institutions have to face a growing presence of frauds and consequently need automatic systems able to support fraud detection and fight.

These systems are essential since it is not always possible or easy for a human analyst to detect fraudulent patterns in transaction datasets, often characterized by a large number of samples, many dimensions and online update.

Project Objectives:

Design, assess and validate a machine learning frame- work able to calibrate in a automatic, real-time and adaptive manner the ATOS Worldline fraud detection strategy.

The goal is to provide the industrial partner with a set of learning tools to be integrated within the credit card fraud detection process daily run by ATOS Worldline in order to improve its robustness, performance and accuracy.